factors influencing ebidding adoption viva defence

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Analyzing Factors that Influence eBidding Utilization in Malaysian Public Sector By Megat Shariffudin B. Hj. Zulkifli (GM03958) 1

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Viva Presentation Slide Online Electronic Reverse Auctions eRAs eBidding Malaysia

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Page 1: Factors Influencing EBidding Adoption Viva Defence

Analyzing Factors that Influence eBidding Utilization in Malaysian Public Sector

By

Megat Shariffudin B. Hj. Zulkifli(GM03958)

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Page 2: Factors Influencing EBidding Adoption Viva Defence

Scope of Presentation

• Introduction• Overview of the eBidding System • Problem Statement• Research Objectives• Research Framework• Hypotheses• Methodology• Data Analysis• Findings• Summary• Policy and Practical Implications• Theoretical Implications• Limitations of the Study• Recommendations for Future Research

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Introduction Malaysian Govt. Aspire for Public Sector Reform to

achieve World-Class Government

e-Government to increase Efficiency, Inter-Agency Cooperation's and Enhanced Service Delivery

Leverage on ePerolehan - Interconnects Suppliers and Buyer via Complete End-to-End Integration Services

eBidding as new innovative G2B procurement auctions (MAMPU, 2005)

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eBidding Overview

Source: (home.eperolehan.gov.my,2009) 4

Page 5: Factors Influencing EBidding Adoption Viva Defence

1. BuyerPosting a Product Request

2.Suppliers Bidding Against Each Other Pushing down Price

S1 S2

S3

3.Buyer Compares the Price Offers and reach Decision

5. Buyer Buys at Lowest Cost

4.Seller Lower Profit but Fast Sale

S3

Reverse Auctions An online procurement auctions performed between multiple

suppliers in real-time via the Internet produces dynamic, competitive process and downward price pressure (Jap, 2007)

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Page 6: Factors Influencing EBidding Adoption Viva Defence

eBidding Overview

1. Acquisition time2. Price Offers3. Procurement decision -making

Conventional/Manual eBidding

- 1 to 3 months- Fixed by Suppliers- Time consuming

- 1 to 14 days- Multiple bids lower prices - Immediately (up to 7 days) ; shorter cycle time for suppliers

Source : http://www.casb.com.my, 2012

Introduced in 2006 as ePerolehan module Electronic bidding mode where the Buyer (Government) get the

Suppliers to compete interactively online to reach lowest price offer to the Buyer

Criteria:o Involve multiple MoF-registered Suppliers with single

procuring agency (Buyer)o Session is online and real-timeo Bidding session is set within a period of time (2 weeks)o Final price is derived from competitive lowest priceo Maximum of 6 sessions can be held at one time

Comparisons:-

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Page 7: Factors Influencing EBidding Adoption Viva Defence

Problem Statements Current eBidding adoption is Low, but planned adoption by

Government is high since Launched in 2006

For example, in 2012, out of 888,866 procurement transactions, only 606 transacting units via eBidding.

Practitioners and Users agree that eBidding is a great idea for cost savings, transparency and shorter cycle time for suppliers, but actual adoption continues to lag.

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Page 8: Factors Influencing EBidding Adoption Viva Defence

eBidding Status

Source : http://home.eperolehan.gov.my/v2/index.php/bm/mengenai-ep/statistik-sistem-ep

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Page 9: Factors Influencing EBidding Adoption Viva Defence

Low Usage Low eBidding utilization detrimental to Government’s

aspiration for e-Government procurement reverse auctions implementation

Losses in terms of development costs, cost and time savings, service delivery efficiency and transparency

Validation issue Gaps in the body of knowledge in terms of public

sector reverse auctions adoption empirical studies Not many empirical studies conducted to ascertain

the causes of low utilization of G2B procurement auctions among the government users.

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Problem Statements

Page 10: Factors Influencing EBidding Adoption Viva Defence

Research Objective

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To examine the User Factors and System Factors that Influence eBidding Utilization among Government Sourcing Officials in Malaysian Public Sector

Page 11: Factors Influencing EBidding Adoption Viva Defence

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To identify the variables that may influence the adoption of eBidding by government users

To examine the effects of the variables on the adoption of eBidding by government users

To examine if some of the variables have moderating or mediating effects on the relationships established as stated in objective 2

To propose a framework to analyze the adoption of eBidding by government users

Specific Objectives

Page 12: Factors Influencing EBidding Adoption Viva Defence

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions

eBidding Adoption

System Quality

Service Quality

Information Quality

H1

H3

H5a

H6a

H2

H4

H1aH2a

H2b

H3a

H4a

H7a

H6

H5

H7

Proposed Research Model

ExperiencePersonal Innovativeness In IT

Satisfaction

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Page 13: Factors Influencing EBidding Adoption Viva Defence

HypothesesH1: Performance expectancy is significantly related to officials’ adoption of eBidding

H2: Effort expectancy is significantly related to officials’ adoption of eBidding

H3: Social influence is significantly related to officials’ adoption of eBidding

H4: Facilitating conditions is significantly related to officials adoption of eBidding

H5: Information quality is significantly related to eBidding adoption

H6: System quality is significantly related to eBidding adoption

H7 : Service quality is significantly related to eBidding adoption

H5a : Satisfaction significantly mediates relationship between information quality and eBidding Adoption

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Page 14: Factors Influencing EBidding Adoption Viva Defence

H6a: Satisfaction significantly mediates relationship between system quality and eBidding Adoption

H7a: Satisfaction significantly mediates relationship between service quality and eBidding Adoption

H1a: PIIT positively moderates the relationship between performance expectancy and eBidding adoption

H2a: PIIT positively moderates the relationships between effort expectancy and eBidding adoption

H2b: Experience negatively moderates the relationship between effort expectancy and eBidding adoption

H3a: Experience negatively moderates the relationship between social influences and eBidding adoption

H4a: Experience positively moderates the relationship between facilitating conditions and eBidding adoption

Continue..

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Quantitative Approach : Survey ; Cross Sectional Study

Population : 2,558 Pusat Tanggung Jawab (PTJs) ; 2,047 ePerolehan-enabled in Peninsular Malaysia

Sampling frame : 1,507 procuring officials 604 PTJs in Klang Valley and Putrajaya.

Unit of Analysis : Individual (Officials as Individual users)

Sampling Procedure : Simple Random Sampling

Research Instruments : Self-administered Questionnaires

The Research Method

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Study InstrumentsSection Variables No. of

ItemsSource

A Performance expectancy (PE)

7 Venkatesh et al., (2003)

B Effort expectancy (EE)

7 Venkatesh et al., (2003)

C Social influence (SI) 7 Venkatesh et al., (2003)

D Facilitating conditions (FC)

7 Venkatesh et al., (2003)

E System quality (SQ) 7 Delone and Mclean, (2003)

F Information quality (IQ)

7 Delone and Mclean, (2003)

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Page 17: Factors Influencing EBidding Adoption Viva Defence

Study InstrumentsSection Variables No. of

ItemsSource

G Service quality (SVQ)

7 Delone and Mclean, (2003)

H Satisfaction 7 Delone and Mclean, (2003)

I Experience 5 Venkatesh et al., (2003)

J Personal Innovativeness in Domain of IT (PIIT)

5 Agarwal and Karahanna, (2000)

K Adoption/Use 4 Delone and McLean, (2003)

L Respondent’s profile 6 Researcher

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Feel for data

Normality

Goodness of data

ReliabilityValidity

Hypothesis testing

Appropriate statistical

(SEM, Hierarchical, Regression)

Testing model fit

RMSEA,TLC, NFI, Chi-

square, etc

Answer for research questions

Data Collection

Data Analysis

Interpretation of Results

Discussion

Data Analysis Process

Source: Sekaran, (2003)

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• SEM - test relationships among variables in the model• Multivariate analysis – show causal dependencies between

endogenous and exogenous variables (Hair et al, 2006)• Confirmatory Factor Analysis (CFA) – measure data

consistency with research model ; Factor Analysis and Path Analysis (Sekaran, 2003)• Pre-Test : Normality ; Reliability and Validity tests • Test Steps :

o Developing a Modelo Path Diagram Relationship o Structural and Measurement Modelso Proposed Model Estimationo Assessing the model Identificationo Evaluate the Goodness of Fit Criteria – TLI, RMSEA,

Chi-square, NFI, CFIo Modifying the Model – re-specification by trimming and

adding paths to achieve model fit

DATA ANALYSIS : STRUCTURAL MEASUREMENT MODELING

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DATA ANALYSIS:NORMALITY Normality test examine data from normal distribution -

examine the central tendency and dispersion

Tests for Mean, Standard Deviation, Skewness and Kurtosis.

Data must be multivariate normality to avoid biased result (Sekaran, 2003)

Data Normality if value of skewness and kurtosis = +-1 (Hisham, 2008)

From the results (Table 10), all data from constructs falls within +-1, hence normally distributed

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DATA ANALYSIS: RELIABILITY

To examine the consistency of respondents in answering the questionnaire items.

Construct reliability measure the degree to which the items were free from random error to produce consistent results (Sekaran, 2003).

Cronbach’s alpha - used in testing consistency reliability between items that is used for multipoint-scaled items Likert scale. .

Cronbach alpha value of 0.5 and higher is considered sufficient in determining reliability of the item (Sekaran, 2003).

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The results indicates all factor loadings for the study constructs are found significant and surpassed the 0.5 guideline recommended by Hair et al., (2006).

All constructs variance extracted estimate surpassed the 50 per cent. The composite reliability values are higher than 0.6 ranging from 0.82 to 0.94.

From the results (Table 11) , the constructs have adequate reliability

DATA ANALYSIS: RELIABILITY

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DATA ANALYSIS: VALIDITY Each constructs tested for discriminant validity

Discriminant validity measures whether one variable is internally correlated, unique and distinct from other variables (Tong, 2007).

A correlation value of 0.5 shows distinct, whereas a correlation value of 0.8 and higher shows a lower distinct.

The results (Table 12) all constructs are less than 0.8 indicating the presence of discriminant validity

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SUMMARY OF HYPOTHESES TESTING

Hypotheses Results

PastEmpirical Studies

H1 Supported Venkatesh et al., (2003) ; Louho et al., (2006) ; Al-Qeisi (2009) 

H2 SupportedHelaiel, (2009); Rosen, (2004); Venkatesh et al., (2003) ; Park et al., (2007) ; Carlsson et al., (2006) ; Gefen and Straud, (2000)

H3 SupportedKarahanna and Straub, (1999); Rosen, (2004); Venkatesh et al., (2003) ; Wolin and Korgaonkar, (2003) ; Singh et al., (2010) ; Amin et al., (2008)

H4 Supported  Hung et al., (2006) ; Venkatesh et al., (2003) ; Wu et al., (2007) ; Joshua and Koshy, (2011)

H5 Supported Delone and Mclean, (2003); Nelson et al., (2005) ; Wang (2008) ; Lee et al., (2007) ; Lin, (2006)

H6Not

supportedNone.

H7Not

supportedHalonen and Martikainen (2011) found service quality of the system is not significant in the use of an IS system

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SUMMARY OF HYPOTHESES TESTING (cont.)

H5aSupported  Wixom and Todd (2005) ; Cronin et al., (1992); Cheung

and Lee, (2005) ; Kim et al., (2009) ; Liu et al., (2000) ; Cora K.L. (2009)

H6aSupported  Wixom and Todd (2005) ; Cronin et al., (1992); Cheung

and Lee, (2005) ; Kim et al., (2009) ; Liu et al., (2000) ; Cora K.L. (2009)

H7aSupported  Wixom and Todd (2005) ; Cronin et al., (1992); Cheung

and Lee, (2005) ; Kim et al., (2009) ; Liu et al., (2000) ; Cora K.L. (2009)

H1aNot

SupportedRosen A., (2004) - found PIIT did not have a moderator role between PE and behaviour intentions

H2aNot

SupportedRosen A., (2004) - found PIIT did not have a moderator role between EE and behaviour intentions

Hypotheses

ResultsPast

Empirical Studies

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Hypotheses

ResultsPast

Empirical Studies

H2b

Not Supported

None. One possible explanation is that users prior experience with similar e-auctions e.g. Lelong and eBay not affected the perception of the IS ease of use

H3a

Not Supported

None. Possible explanation is that users past experience not affected the effects of peer pressure of using IS

H4aNot

Supported

None. Users prior experience not affected the perception of availability of infrastructure/facilities supporting the system.

SUMMARY OF HYPOTHESES TESTING (cont.)

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Page 27: Factors Influencing EBidding Adoption Viva Defence

SUMMARY• The user behavior factors (performance expectancy ;

effort expectancy; social influence & facilitating conditions) significantly related to eBidding adoption, including IQ.

• System quality and service quality are proven to be not significantly associated with eBidding adoption

• Experience and personal innovativeness in IT (PIIT) are confirmed not to exhibit moderating effects on relationships between user factors and eBidding adoption

• Satisfaction is found to have a full mediating effect on system quality and adoption and partial mediating effect on information quality with adoption and service quality with adoption of eBidding

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POLICY AND PRACTICAL IMPLICATIONS

• Improving e-procurement auctions policy with better understanding user behaviour – to incorporate PE, EE, SI, FC and users satisfaction factors in policy planning.

• Program managers and the early adopters of the eBidding should communicate the usefulness to peers about the benefits of using the eBidding

• Support eBidding use through review of policy and circulars relevant to the eBidding system e.g. mandatory use

• eBidding is reliable and productive system, can be improved by considering other attributes, such as, product specifiability, value and price.

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IMPLICATIONS FOR THEORY• Combination of determinants from various disciplines

• Validation of UTAUT theory in G2B reverse auctions setting

• Provide empirical support that the eBidding adoption is influenced by system factors mediated by user satisfactions.

• In terms of methodological implications, SEM is recommended for model testings.

• There are various benefits of SEM over other multivariate techniques. SEM can provide estimates of error variance parameters, while multivariate techniques are not able to correct measurement error.

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LIMITATIONS OF THE STUDY

•Research done only in the Putrajaya, Klang Valley and Seremban

•Sample size as most common SEM estimation procedure is MLE with minimum sample size of 150 - 200 cases (Hair et al, 2006)

•A cross sectional study but not a longitudinal study

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• Studies in other areas in the Peninsular and Sabah Sarawak.

• Further study with the inclusion of the suppliers

• Future studies in a longitudinal context

• Incorporate determinants from reverse auctions attributes (i.e. value, product specifications, competitiveness)

RECOMMENDATIONS FOR FUTURE RESEARCH

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THANK YOU

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Notes

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Page 34: Factors Influencing EBidding Adoption Viva Defence

Problems Statemen

t& Study

Objectives

Global eG Development

Malaysia’s eGovernment

ePerolehan

Online Reverse Auctions

Overview of eBidding

Overview of Established theories of User AcceptanceTAM1,Diffusion of Innovation

UTAUTIS Success Model

PIIT

Theoretical and

Empirical Studies User

Factors-> Behavioral Intention

System Quality Factors–> Behavioral Intention

Personal Innovativeness on IT–> Behavioral IntentionModels Comparisons

Integrative Model Proposed

Theoretical and

Empirical Studies User &

System Quality Factors–> Adoption Behavior

Mediator role of User Satisfaction

Moderator Factors on the User Factors -> Adoption Behavior

LiteratureLiterature

Review

Page 35: Factors Influencing EBidding Adoption Viva Defence

The study and the model proposed will be based on:

The Relevant Theories

Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003)An Information System (IS) framework for assessing an individual’s intention to use an IS technologyInformation System Success Model (Delone and Mclean, 2003)A system success can be evaluated in terms of information, system, and service quality; these characteristics affect the subsequent use or intention to use and user satisfactionPersonal Innovativeness in Information Technology (Agarwal and Prasad, 1998)Domain-specific individual trait which reflects the willingness of a person to try out a new information technology

1

2

3

Page 36: Factors Influencing EBidding Adoption Viva Defence

Theoretical Models

Moderator Model of Personal Innovativeness in Information Technology (PIIT) (Agarwal & Prasad, 1998)

Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003)

IS Updated Success Model (Delone and Mclean, 2003)

Page 37: Factors Influencing EBidding Adoption Viva Defence

  

No Construct Factor loading

Average Variance Extracted

(AVE)

Composite Reliability

Cronbach's Alpha

1.Performance Expectancy (PE) .75 .92 .929

  PE1 .888  PE2 .949  PE3 .831  PE4 .798

2. Effort Expectancy (EE) .79 .94 .932

  EE4 .948  EE5 .974  EE6 .897  EE7 .703  

3. Social Influence (SI)

  SI 1 .873 .7 .9 .908  SI 3 .879  SI 5 .686  SI 7 .888

4.Facilitating Conditions (FC)

.886

  FC2 .787 .66 .88  FC 4 .763  FC 5 .733  FC 7 .95 3

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5. Information Quality (IQ) .891

  IQ2 .822 .7 .9  IQ4 .803  IQ5 .735  IQ6 .957

6. System Quality (SYQ).72 .91

 .891

  SYQ3 .568  SYQ5 .906  SYQ6 .899  SYQ7 .969

7. Service Quality (SVQ) .65 .88.878

  SVQ1 .803  SVQ3 .883  SVQ5 .78  SVQ6 .7548. Actual Use (USE) .67 .89 .892  USE1 .756  USE2 .934  USE3 .911  USE 4 .6389. Satisfaction .53 .82 .818  Satisfaction1 .76  Satisfaction2 .66  Satisfaction3 .69  Satisfaction4 .80      

  

No Construct Factor loading

Average Variance Extracted

(AVE)

Composite Reliability

Cronbach's Alpha

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Page 39: Factors Influencing EBidding Adoption Viva Defence

Variable X1 X2 X3 X4 X5 X6 X7 X8 X9

PE (X1) 1

Adoption/Use (X2) .660** 1

EE (X3) .643** .771** 1

SI (X4) .462** .809** .605** 1

FC (X5) .129 .406** .242** .622** 1

SQ (X6) .683** .542** .425** .379** .039 1

IQ (X7) .423** .698** .513** .798** .785** .328** 1

SVQ (X8) .437** .721** .543** .723** .561** .358** .675** 1

Satisfaction (X9) .710** .815** .696** .570** .191* .557** .482** .522**1

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

DATA ANALYSIS: VALIDITY

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